In a traditional setting, machine learning models are trained under empirical risk minimization (ERM) framework, where train and test set are assumed to be sampled
from the same distribution. These models are optimized to work well across the entire data distribution. However, this training setup would not be ideal when at
test time only a subset of train distribution is of interest. This situation can come up in various practical problems: (1) targeting a certain demographic for
recommender systems [1]}, (2) personalizing models in health for a certain anomaly or demographic [2]}, [3]}, etc.
In this setting, an important question one needs to answer is: What training data should I train the model on?
